Canada's forest and peatland ecosystems are globally significant carbon stores, whose management will be influenced by climate change mitigation policies such as offset systems. To be effective, these policies must be grounded in objective information on the relationships between land use, ecosystem carbon dynamics, and climate. Here, we present the outcomes of a workshop where forest, peatland, and climate experts were tasked with identifying management actions required to maintain the role of Canada's forest and peatland ecosystems in climate regulation. Reflecting the desire to maintain the carbon storage roles of these ecosystems, a diverse set of management actions is proposed, incorporating conservation, forest management, and forest products.Key words: forests, peatlands, carbon, Canada, climate change, management, forest products, conservation RÉSUMÉLes écosystèmes du Canada formés par les forêts et les tourbières constituent des réservoirs importants de carbone dont l'aménagement sera influencé par les politiques d'atténuation des effets des changements climatiques comme les systèmes de crédits compensatoires. Ces politiques, si elles se veulent efficaces, doivent être rattachées à des informations objectives sur les relations entre l'utilisation du territoire, la dynamique du carbone de ces écosystèmes et le climat. Dans ce texte, nous présentons les conclusions d'un atelier au cours duquel on a demandé à des experts du secteur des forêts, des tourbières et du climat d'identifier les actions à entreprendre en aménagement pour préserver le rôle régulateur des écosystèmes formés des forêts et des tourbières au Canada face au climat. Tout en reflétant le souci de maintenir le rôle de réservoir de carbone joué par ces écosystèmes, un ensemble d'actions à entreprendre en aménagement est proposé, incorporant la conservation, l'aménagement forestier et les produits forestiers.

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Long-term exposure to elevated indoor radon concentrations has been determined to be the second leading cause of lung cancer in adults after tobacco smoking. With the establishment of a National Radon Program in Canada in 2007 thousands of homes across the country have been tested for radon. Although the vast majority of people are exposed to low or moderate radon concentrations; from time to time; there are homes found with very high concentrations of radon. Among those living in homes with very high radon concentrations, it is typically parents of young children that demonstrate a great deal of concern. They want to know the equivalent risk in terms of the lifetime relative risk of developing lung cancer when a child has lived in a home with high radon for a few years. An answer to this question of risk equivalency is proposed in this paper. The results demonstrate clearly that the higher the radon concentration; the sooner remedial measures should be undertaken; as recommended by Health Canada in the Canadian radon guideline.

In recent years, the incidence of human brucellosis (HB) in the Shanxi province has ranked to be the top five among the 31 China provinces. HB data in Shanxi province between 2011 and 2016 were collected from the Centers for Disease Control and Prevention. Spatial and temporal distribution of HB was evaluated using spatial autocorrelation analysis and space-time scan analysis. The global Moran’s I index ranged from 0.37 to 0.50 between 2011 and 2016 (all P < 0.05), and the “high-high” clusters of HB were located at the northern Shanxi, while the “low-low” clusters in the central and southeastern Shanxi. The high-incidence time interval was between March and July with a 2-fold higher risk of HB compared to the other months in the same year. One most likely cluster and three secondary clusters were identified. The radius of the most likely cluster region was 158.03 km containing 10,051 HB cases. Compared to the remaining regions, people dwelling in the most likely region were reported 4.50-fold ascended risk of incident HB. HB cases during the high-risk time interval of each year were more likely to be younger, to be males or to be farmers or herdsman than that during the low-risk time interval. The HB incidence had a significantly high correlation with the number of the cattle or sheep especially in the northern Shanxi. HB in Shanxi showed unique spatio-temporal clustering. Public health concern for HB in Shanxi should give priority to the northern region especially between the late spring and early summer.

Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.